In 2019, the U.S. Geological Survey (USGS), in cooperation with the National Park Service, initiated a study using surrogate technology to predict real-time metallic-contaminant concentrations (MCCs) in the Clark Fork at two USGS streamgages that bracket Grant-Kohrs Ranch National Historic Site (GRKO) near Deer Lodge, Montana. Clark Fork at Deer Lodge(streamgage 12324200), Mont., about one mile upstream from GRKO, and Clark Fork above Little Blackfoot River near Garrison (streamgage 12324400), Mont., about 12 miles downstream from GRKO property were instrumented with turbidity and acoustic sensors for monitoring the Clark Fork during National Park Service Superfund remediation activities. Time-series data from backscatter signals from fixed-point turbidity and acoustic sensors were correlated with discrete MCC samples collected from the Clark Fork and were used as surrogates for estimating real-time cadmium, copper, iron, lead, manganese, zinc, and the metalloid trace element arsenic. A stepwise regression approach was used to develop statistical models to predict MCCs based on instantaneous values of turbidity and acoustic backscatter. Simple linear regression models using turbidity as the sole explanatory variable produced the best models with R-squared values exceeding 0.90 in 9 of 12 models. Nash-Sutcliffe Efficiency values were used to evaluate the effectiveness of predictive models to approximate measured MCCs, and model biases were calculated as an additional check on model accuracy. The R-LOADEST statistical package was used to compute annual and daily metallic-contaminant loads along with 95-percent prediction intervals. R-LOADEST loads were compared to time-series computed loads to evaluate the applicability of time-series data for calculating daily and annual metallic-contaminant loads. Results from annual load estimates indicated an increase in loads for all metallic contaminants between the two monitoring sites. These results provided real-time information to National Park Service management for evaluating variation in water quality during Superfund remediation, comparing MCC values relative to aquatic life standards, and will help quantify benefits from Superfund remediation activities.